LoPECS: A Low-Power Edge Computing System for Real-Time Autonomous Driving Services

Tang, Jie; Liu, Shaoshan; Liu, Liangkai; Bo, Yu; Shi, Wei

doi:10.1109/access.2020.2970728

Cited by 42 publications

(37 citation statements)

References 35 publications

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“…Few works have used real network experiments to evaluate their proposals of offloading in vehicular networks. Among these are [180], [76], and [182].…”

Section: A: Realmentioning

confidence: 99%

“…Some real world environments were also created for testing at Aldenhoven Testing Center (ATC) and Mcity (a test facility by the University of Michigan, USA) [191]. Some works used real mobility for offloading, such as [180], [182], and [84].…”

Section: A: Realmentioning

confidence: 99%

“…Some works used real and simulated mobility [136], [133], [182]. However, they were not simultaneous experiments as if it were an emulation.…”

Section: B: Simulationmentioning

confidence: 99%

“…In the case of university campuses and industrial parks, these are scenarios characterized by low vehicle density, low vehicle speed and traffic generally limited to authorized vehicles. Some of these scenarios were used in [180] and [182].…”

Section: C: Othermentioning

confidence: 99%

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Computation Offloading for Vehicular Environments: A Survey

et al. 2020

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With significant advances in communication and computing, modern day vehicles are becoming increasingly intelligent. This gives them the ability to contribute to safer roads and passenger comfort through network devices, cameras, sensors, and computational storage and processing capabilities. However, to run new and popular applications, and to enable vehicles operating autonomously requires massive computational resources. Computational resources available with the current day vehicles are not sufficient to process all these demands. In this situation, other vehicles, edge servers, and servers in remote data centers can help the vehicles by lending their computing resources. However, to take advantage of these computing resources, computation offloading techniques have to be leveraged to transfer tasks or entire applications to run on other devices. Such computation offloading can lead to improved performance and Quality of Service (QoS) for applications and for the network. However, computation offloading in a highly dynamic environment such as vehicular networks is a major challenge. Therefore, this survey aims to review and organize the computation offloading literature in vehicular environments. In addition, we demystify some concepts, propose a taxonomy with the most important aspects and classify most works in the area according to each category. We also present the main tools, scenarios, subjects, strategies, objectives, etc., used in the works. Finally, we present the main challenges and future directions to guide future research in this active research area.

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“…Few works have used real network experiments to evaluate their proposals of offloading in vehicular networks. Among these are [180], [76], and [182].…”

Section: A: Realmentioning

confidence: 99%

Section: A: Realmentioning

confidence: 99%

“…Some works used real and simulated mobility [136], [133], [182]. However, they were not simultaneous experiments as if it were an emulation.…”

Section: B: Simulationmentioning

confidence: 99%

Section: C: Othermentioning

confidence: 99%

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Computation Offloading for Vehicular Environments: A Survey

et al. 2020

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“…Furthermore, a novel convolution method that minimizes data movement by reusing both the feature map and the convolution kernel without any additional control is proposed. Therefore, the proposed implementation achieves reasonable silicon area, low power consumption, and good performance and it can be used for edge computing applications such as drones, autonomous vehicles, and on-device artificial intelligence (AI) [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

CENNA: Cost-Effective Neural Network Accelerator

Park

Chung

2020

Electronics

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Convolutional neural networks (CNNs) are widely adopted in various applications. State-of-the-art CNN models deliver excellent classification performance, but they require a large amount of computation and data exchange because they typically employ many processing layers. Among these processing layers, convolution layers, which carry out many multiplications and additions, account for a major portion of computation and memory access. Therefore, reducing the amount of computation and memory access is the key for high-performance CNNs. In this study, we propose a cost-effective neural network accelerator, named CENNA, whose hardware cost is reduced by employing a cost-centric matrix multiplication that employs both Strassen’s multiplication and a naïve multiplication. Furthermore, the convolution method using the proposed matrix multiplication can minimize data movement by reusing both the feature map and the convolution kernel without any additional control logic. In terms of throughput, power consumption, and silicon area, the efficiency of CENNA is up to 88 times higher than that of conventional designs for the CNN inference.

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Intelligent In‐Vehicle Interaction Technologies

Murali

Kaboli

Dahiya

2021

Advanced Intelligent Systems

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Autonomous vehicles (AVs) will soon become a reality with almost every major automotive original equipment manufacturer (OEM) such as Tesla, BMW, Daimler, and big technology giants such as Google's Waymo and Apple Car pushing toward the full self-driving objective. According to the SAE J3016 taxonomy [1] of autonomous driving, there are six levels of automated driving systems ranging from level 0 (completely manual) to level 5 (full selfdriving [FSD]) automated systems that are expected to function in all geographic locations, all weather conditions, and under all conditions. The proposed benefits of intelligent vehicles include fewer road accidents, enhanced safety, reduced traffic congestion, effective commute time usage, and importantly enjoyable and comfortable ride. With increased autonomy, the drivers also assume the role of mere passengers who are engaged in nondriving activities and are unavailable to participate in traffic interactions. This would increase complexity in a mixed-autonomy traffic environment as interactions with pedestrians and cyclists are based on visual cues with the driver. Therefore, intelligent vehicles also need to autonomously interact with other traffic participants such as pedestrians and cyclists and other vehicles.Human-vehicle interaction (HVI) is closely related to the field of human-robot interaction (HRI). It entails the problem of understanding and shaping the interaction dynamics between humans and vehicles. Particularly, the domain of interaction involves the study of sensation, perception, information exchange, inference, and decision-making. [2] With increasing levels of automation, the driver will have more time and choice to perform various tasks other than driving and this opens new avenues for interaction. As a result, a growing number of sensors are being incorporated into the vehicles to understand the driver's and/or passenger's actions, emotions, and personal choices to offer the precise functionalities and services for an enjoyable ride. [3] Figure 1 shows the schematic of in-vehicle interaction consisting of various interactive interfaces and sensing modalities present in the vehicle as well as some modes of interaction such as gestures, speech, and eye gaze.The advances in HVI have been presented in previous review articles, which have mainly focused on the detection of particular characteristics for driving assistance such as driver attention detection, [4] emotion recognition, [5] drowsiness detection, [6] mental workload, [7] and so on. A few other articles have reviewed the interior designs of AVs to better support and interact with drivers and passengers. [8,9] Likewise, the user interfaces (UIs) and user experience (UX) of vehicle interiors have been studied in context with manual as well as automated driving. [10] A number of previous works have also studied the interaction with the external

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LoPECS: A Low-Power Edge Computing System for Real-Time Autonomous Driving Services

Cited by 42 publications

References 35 publications

Computation Offloading for Vehicular Environments: A Survey

Computation Offloading for Vehicular Environments: A Survey

CENNA: Cost-Effective Neural Network Accelerator

Intelligent In‐Vehicle Interaction Technologies

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